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This is an assignment I did in ESM 244 on CA Oil Spills.

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knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)

library(spatstat)
library(maptools) 
library(raster) 
library(tidyverse)
library(here)
library(sf)
library(tmap)
library(lubridate)

Overview:

Here, I explore and visualize data from the California Department of Fish and Wildlife Oil Spill Tracking database. First, I created an interactive map for users to view oil spill incidents by county. I, then, created a choropleth map in ggplot in which the fill color for each county depends on the count of inland oil spill events by county for the 2008 oil spill data. These data are important for the CDFW Office of Spill Prevention and Response field responders, as well as researchers (ecologists, toxicologist, etc.).

Data citation: Data made available by the California State GeoPortal: Department of Fish and Wildlife Oil Spill Incident Tracking.

Exploratory Interactive Map:

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# First I  need to get CA county outlines 

# Read in CA county shapefile
ca_counties_sf <- read_sf(here("oilspilldata", "CA_Counties_TIGER2016.shp")) 

# Simplify CA counties to only include county name and land area 
ca_subset_sf <- ca_counties_sf %>% 
  janitor::clean_names() %>%
  select(county_name = name, land_area = aland)

head(ca_subset_sf) ### WARN AGAINST View()

# Check and set the CRS
ca_subset_sf %>% st_crs() # EPSG: 3857
ca_subset_sf %>% raster::crs() ### to show proj4 string

# Plot it 
ggplot(data = ca_subset_sf) +
  geom_sf(aes(fill = land_area), color = "white", size = 0.1) +
  theme_void() +
  scale_fill_gradientn(colors = c("grey","white","brown"))

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# Oil spill spatial points

# read in data 
oil_spill_sf <- read_sf(dsn = here("oilspilldata"), 
                        layer = "ds394") %>% 
  janitor:: clean_names() %>% # make all lowercase 
  select(localecoun) # select only counties 

# Check the CRS
oil_spill_sf %>% st_crs() # EPSG code: 3310, *note that this is different from the California counties CRS, so we'll want to update it to match.
oil_spill_sf %>% raster::crs()

# Update the CRS to match CA counties CRS
oil_spill_3857_sf <- st_transform(oil_spill_sf, st_crs(ca_counties_sf))

# plot CA counties and oil spills together!
ggplot() +
  geom_sf(data = ca_subset_sf) +
  geom_sf(data = oil_spill_3857_sf, size = 1, color = "red")

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# Make it interactive!
# Set the viewing mode to "interactive":
tmap_mode(mode = "view")

# Map
tm_shape(ca_subset_sf) +
  tm_fill("land_area", palette = "Greens") +
  tm_shape(oil_spill_sf) +
  tm_dots()

Figure 1: Exploratory interactive map in tmap showing the location of oil spill events in California by county.

Chloropeth Map

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#new df
oil_spill_new_sf <- read_sf(dsn = here("oilspilldata"), 
                        layer = "ds394") %>% 
  janitor:: clean_names()

# update CRS
oil_spill_new_3857_sf <- st_transform(oil_spill_new_sf, st_crs(ca_counties_sf))

# Need to join data
ca_oil_sf <- ca_subset_sf %>% 
  st_join(oil_spill_new_3857_sf)

head(ca_oil_sf)

# Need to subset the data to include year and county
oil_spill_2008_sf <- ca_oil_sf %>% 
  mutate(date_new = as_date(dateofinci, format="%m/%d/%y")) %>% 
  mutate(year = year(date_new)) %>% 
  filter(inlandmari %in% "Inland") %>% 
  select(localecoun, year, inlandmari) # select only county and year 


# find count of inland oil spill events by county for 2008
oil_counts_sf <- oil_spill_2008_sf %>%
  group_by(localecoun) %>%
  summarize(spill_count = sum(!is.na(localecoun)))

head(oil_counts_sf)

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# Plot a chloropleth!
ggplot(data = oil_counts_sf) +
  geom_sf(aes(fill = spill_count), color = "white", size = 0.1) +
  scale_fill_gradientn(colors = c("lightgray","orange","red")) +
  theme_minimal() +
  labs(fill = "Number of Inland Oil Spills in 2008")

Figure 2: Choropleth map in ggplot where the fill color for each county depends on the number of inland oil spill events by county for the 2008 data.